Static, deterministic bid evaluation methods in construction procurement consistently fail to account for market volatility and decision uncertainty. To mitigate these systemic inefficiencies, this study develops an integrated computational framework that bridges micro-level individual bid evaluation with macro-level portfolio resource allocation. Using 410,963 construction public records, the architecture synthesizes three elements: Bayesian-tuned random forests for probabilistic outcome prediction, neutrosophic multi-criteria decision analysis (MCDA) to model predictive ambiguity and soft actor-critic reinforcement learning (RL) agents to autonomously optimize decision weights. Validation through ex-post strategic portfolio simulations demonstrates that this neuro-symbolic approach outperforms traditional static heuristics across all strategic targets. The dynamic risk-averse RL policy reduced the aggregate average Corruption Risk Index (CRI) by 74.37% compared to the static expert baseline, achieving an optimized CRI of 0.029. Furthermore, the competition-focused policy improved market participation metrics by 26.05% over baseline assessments. By shifting procurement from a subjective administrative task to an auditable, data-driven process, this research provides a scalable mechanism for standardizing routine tender evaluations while dynamically adapting risk parameters for high-value, complex construction portfolios.

Social value has emerged as a key dimension in assessing the broader impacts of construction projects, reflecting their contribution to community well-being and sustainable development. Despite its growing relevance, quantitative investigation into the factors influencing project social value remains limited, particularly in developing countries. This study aims to identify and examine success factors influencing the achievement of project social value in road construction projects in Indonesia using Multiple Regression Analysis. Data were collected from 100 respondents representing the government, contractors, consultants, and communities, using a structured questionnaire. Five independent variables were analysed, namely community engagement, government role, project sustainability practices, project organisational performance, and design. The regression model explains 76.7% of the variance in project social value (R 2 = 0.767), with design emerging as the strongest predictor (β = 0.615, p < 0.001). These findings indicate that project social value in road projects is more strongly influenced by upstream project factors, particularly design, than by direct construction activities. This highlights the strategic importance of design in strengthening socially sustainable infrastructure and provides evidence-based insights to support policy development for future road projects in emerging economies.

Cost estimation is a critical preconstruction function increasingly targeted for artificial intelligence (AI)-enabled improvement, yet empirical evidence on how professional estimators integrate these tools into daily practice remains limited. This exploratory qualitative study examines AI adoption in construction cost estimation through semi-structured interviews with 12 professional estimators from eight U.S.-based cost consultancies and general contractors, supplemented by a corroborating interview with a senior technology executive. Drawing on the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory as complementary frameworks, the analysis operationalizes seven theoretical constructs at the coding level and identifies six practitioner-validated priority themes: reliability and uncertainty signaling, workflow integration, systems coverage for mechanical, electrical, and plumbing (MEP) assemblies, market-linked pricing, document and scope intelligence, and human-centered automation. The study introduces and formally defines the verification paradox, a previously unnamed mechanism in which estimators must re-perform manual takeoffs to validate AI outputs, neutralizing efficiency gains. The six themes are operationalized into the Construction AI Adoption Readiness Assessment (CAARA), an 18-item diagnostic instrument mapped to a three-phase implementation roadmap, presented as a conceptual framework pending psychometric validation. This study contributes practitioner-grounded empirical evidence and a structured diagnostic tool to support phased AI integration in professional estimation contexts.

Accurate estimation of construction task duration is crucial for project management. This paper introduces a framework to compare the predictive accuracy of learning curve (LC) analysis and Bayesian estimation under limited on-site information. In a study on high-rise formwork operation, we fitted seventeen LC models across seven unit types, with selection through out-of-sample error on unit values. Bayesian analysis utilized priors that encoded expert judgment error via the coefficient of variation and compared three likelihoods using the Bayes factors. We combined Maximum Likelihood Estimation and Bayesian outputs with the LC in simulation. Analysis was repeated with increasing data, emulating project progress. Prior specification dominated early-stage accuracy, and low expert error amplified gains from well-aligned priors and harms from misspecification, with sensitivity diminishing near 20 data points. Rather than choosing the lowest error LC, it was beneficial to track equations below 10% test error and tighten to 5% with 20 data points. The LC, a point estimator, achieved the lowest continuous ranked probability score. Comparing mean absolute error of probabilistic estimates’ modes also confirmed the LC as the best estimator for this dataset. Introduced framework advances theory and practice through reproducible comparison of productivity models to inform decisions under data scarcity.

The demand for sustainable building practices that address climate change has grown, prompting interest in nature-based solutions like mycelium, the root structure of fungi, in the construction industry. However, there is a relative paucity of research on such innovations within the South African construction industry. This study explores the potential application of mycelium and the challenges hindering its adoption in South Africa’s construction sector, using a quantitative research approach through a questionnaire survey. Data were analysed using mean item scores, the Kruskal-Wallis H-test, exploratory factor analysis (EFA), and partial least squares structural equation modelling (PLS-SEM). Findings highlight a significant gap in awareness among construction professionals regarding mycelium. EFA identifies two main challenge categories: issues related to perception and competence, and concerns about costs and regulations. Addressing these challenges requires a concerted effort from all stakeholders, including government, industry, academia, and communities. By developing approaches to enhance knowledge and understanding, and by prioritising and investing in research and development on mycelium and other NbS, the South African construction industry can advance toward a more sustainable future. This study provides valuable insight into the application of NbS, such as mycelium, from a South African perspective, where such research is currently scarce.

The construction industry is one of the major contributors to global environmental degradation due to its high carbon emissions, excessive resource consumption, and large volumes of construction and demolition waste. In response to this issue, this study explores the integration of Building Information Modeling (BIM) with Life Cycle Assessment (LCA) to assess the environmental performance of green concrete incorporating recycled coarse aggregate (RCA). A systematic literature review was conducted to explore the convergence of BIM, LCA, circular economy (CE) principles, and green concrete technologies. Based on the identified research gaps, a BIM-integrated LCA framework was developed to assess environmental indicators—such as global warming potential, water footprint, and resource depletion—under different RCA alternative scenarios. The model was applied to a real administrative building project in Vietnam, where concrete mixes with different RCA contents (0, 30, 60, and 100%) were evaluated using the Tally plugin and the Ecoinvent v3.8 database. The results showed that while the use of RCA can reduce water consumption and some toxicity indicators, it can increase carbon emissions due to the higher cement demand required to maintain compressive strength. These trade-offs highlight the need for cement reduction and material optimization strategies to be incorporated early in the design phase. By embedding environmental indicators into the BIM environment, the approach supports scenario-based, data-driven decision-making in line with CE objectives. These findings demonstrate the potential of accessible and low-cost BIM-LCA tools to increase transparency, enable circular material tracking, and inform sustainable design practices. The model provides a replicable approach to integrating digital workflows and green material choices in construction, contributing to broader carbon reduction and zero waste goals.

The principal contractor appointed to manage a construction site has a legal duty to develop a positive psychosocial safety climate (PSC) that protects all workers from psychological risk and harm. On construction sites, tradeswomen remain a minority group and are more susceptible to workplace stressors due to their gender. A survey was completed by 143 women in trade and labour roles in commercial and civil construction sectors to examine the rel′ationship between PSC with supervisor support and respect. Partial least squares structural equation modelling was used to test a proposed research model and hypotheses. Findings identified that PSC is positively associated with respectful treatment of workers by men and women. PSC also significantly predicts supervisor support, which in turn has a significant influence on respectful treatment of workers by men. No significant relationship was found between supervisor support and respectful treatment of workers by women. Findings highlight the importance of creating a psychosocially safe work environment which influences workplace respect both directly and indirectly through supervisor support. The principal contractor can mitigate gender-based psychosocial risks by implementing policies, practices, and standards of behaviour at the construction site which create and sustain a psychosocial safety climate for women.

Construction disputes in metro-rail infrastructure projects have traditionally been linked to unclear contractual provisions, technical shortcomings, and inadequate coordination among stakeholders. This study aims to evaluate 138 construction dispute drivers in a mega infrastructure project like metro-rail construction through application of a fuzzy Multi-Criteria-Decision-Making (MCDM) framework like Fuzzy VIKOR to identify, prioritize, and evaluate the most critical, legal and operational dispute drivers. Out of 138 dispute drivers depending upon the lowest values of S ˜ i ​(group-utility) and R ˜ i (individual-regret), 30 dispute divers have been considered in order of priority with the lowest value factor getting the first rank. Furthermore, top 10 dispute drivers were identified based on Q ˜ i (VIKOR-compromise ranking-index-value). The principal research contribution and novelty of the study lies in establishing a quantitatively validated and sensitivity-tested prioritization model that explicitly captures the legal, regulatory, environmental, and governance dimensions of dispute formation. The findings reveal that the top dispute driver is climate-related compliance failures, particularly inability to ensure climate resilience. Sensitivity analysis confirms the structural stability of these rankings, with stability indices exceeding 0.91, establishing the robustness and legal reliability of the prioritization outcomes. The practical applications of this study are substantial and directly relevant to owners, contractors, policymakers, and legal professionals.

Infrastructure organisations rely on heavy equipment and operate in environments characterised by complexity, scale, and interdependence, and are vulnerable to disruptions that affect project delivery. In such contexts, resilience should be embedded in management practices. However, research on how equipment management practices (CEMP) contribute to resilience in these equipment-intensive infrastructure organisations remains limited. Grounded in Teece’s dynamic capabilities (DC) framework, this study positions resilience as the focal outcome and conceptualises organisational learning (OL) as a micro-foundation for the development and flexible deployment of DC. PLS-SEM analysis revealed that routine practices provide immediate operational stability, contributing directly to resilience, but their impact is more strongly realised through DC. While CEMP provides the foundational routines, OL serves as a micro-foundation and enabling their transformation into DCs, enhancing flexible deployment. DCs act as the primary mechanism through which these routines contribute to resilience against disruptions. This study advances the knowledge by demonstrating how resilience emerges through the interplay of routine practices, DC, and learning-enabled adaptive flexibility, transitioning operational stability into resilience. It offers practitioners empirical evidence that resilience is not built by mere investment in equipment, but by learning-enabled capabilities that transform operational routines into flexibly deployed adaptive performance under uncertainty.